Design and evaluation of an achievability contour display for piloted lunar landing

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 81-83).Landing on the moon requires the selection and identification of a location that is level and free of hazards, along with a stable, controlled descent to the lunar surface through the use of automated systems and manual control. Spatial disorientation may occur upon reentering a gravitational field after vestibular adaptation to microgravity during lunar transit. The workload associated with selecting a suitable landing point based on the remaining fuel and current vehicle states is a concern. In Apollo, visual out-the-window information was heavily relied upon to support the selection of a landing point, and there was little support information available to indicate whether the desired site was achievable. A novel achievability contour display element showing the dynamic achievable landing area was developed based on a Goal-Directed Task Analysis and usability testing. A subject experiment was conducted in a lunar landing simulation environment to test the effects of the achievability contour on pilot performance, situation awareness, and workload in simulated approach and terminal descent scenarios as compared to an Apollo-style auditory display. Two control modes were used: supervisory control and roll, pitch, and yaw rate-control/attitude-hold (RCAH) manual control. The experiment also investigated differences in display effect with and without a required redesignation. Results of the subject experiment (N = 10) indicate that the achievability contour display showed significant improvement in subjective situation awareness and workload ratings. The results also indicate a change in decision-making behavior with the use of the achievability contour display. There was no measurable difference in flight and landing performance measures between the two display conditions. The results of the experiment suggest that providing the achievability contour display may have beneficial effects on pilot situation awareness and workload during the final approach and terminal descent maneuvers. Additional research is needed to determine the optimal implementation and pilot interaction methods in the use of this display.by Alexander J.Stimpson.S.M

Exploring Concepts of Operations for On-Demand Passenger Air Transportation

In recent years, a surge of interest in "flying cars" for city commutes has led to rapid development of new technologies to help make them and similar on-demand mobility platforms a reality. To this end, this paper provides analyses of the stakeholders involved, their proposed operational concepts, and the hazards and regulations that must be addressed. Three system architectures emerged from the analyses, ranging from conventional air taxi to revolutionary fully autonomous aircraft operations, each with vehicle safety functions allocated differently between humans and machines. Advancements for enabling technologies such as distributed electric propulsion and artificial intelligence have had major investments and initial experimental success, but may be some years away from being deployed for on-demand passenger air transportation at scale

A Model-Based Measure to Assess Operator Adherence to Procedures

Procedures play an important role in domains where humans interact with critical, complex systems. In such environments, the operator’s ability to correctly follow a given set of procedures can directly impact system safety. A quantitative measure of procedural adherence during training for complex system operation would be useful to assess trainee performance and evaluate a training program. This paper presents a novel model-based objective metric for quantifying procedural adherence in training. This metric is sensitive to both the number and nature of procedural deviations, and can be used with cluster analysis to classify trainee performance based on adherence. The metric was tested on an experimental data set gathered from volunteers using aircraft maintenance computer-based training (CBT). The properties of the metric are discussed, along with future possibilities

Application of targeted molecular and material property optimization to bacterial attachment-resistant (meth)acrylate polymers

Developing medical devices that resist bacterial attachment and subsequent biofilm formation is highly desirable. In this paper, we report the optimization of the molecular structure and thus material properties of a range of (meth)acrylate copolymers which contain monomers reported to deliver bacterial resistance to surfaces. This optimization allows such monomers to be employed within novel coatings to reduce bacterial attachment to silicone urinary catheters. We show that the flexibility of copolymers can be tuned to match that of the silicone catheter substrate, by copolymerizing these polymers with a lower Tg monomer such that it passes the flexing fatigue tests as coatings upon catheters, that the homopolymers failed. Furthermore, the Tg values of the copolymers are shown to be readily estimated by the Fox equation. The bacterial resistance performance of these copolymers were typically found to be better than the neat silicone or a commercial silver containing hydrogel surface, when the monomer feed contained only 25 v% of the “hit” monomer. The method of initiation (either photo or thermal) was shown not to affect the bacterial resistance of the copolymers. Optimized synthesis conditions to ensure that the correct copolymer composition and to prevent the onset of gelation are detailed

Measurement and Modeling of Particle Radiation in Coal Flames

This work aims at developing a methodology that can provide information of in-flame particle radiation in industrial-scale flames. The method is based on a combination of experimental and modeling work. The experiments have been performed in the high-temperature zone of a 77 kWth swirling lignite flame. Spectral radiation, total radiative intensity, gas temperature, and gas composition were measured, and the radiative intensity in the furnace was modeled with an axisymmetric cylindrical radiation model using Mie theory for the particle properties and a statistical narrow-band model for the gas properties. The in-flame particle radiation was measured with a Fourier transform infrared (FTIR) spectrometer connected to a water-cooled probe via fiber optics. In the cross-section of the flame investigated, the particles were found to be the dominating source of radiation. Apart from giving information about particle radiation and temperature, the methodology can also provide estimates of the amount of soot radiation and the maximum contribution from soot radiation compared to the total particle radiation. In the center position in the flame, the maximum contribution from soot radiation was estimated to be less than 40% of the particle radiation. As a validation of the methodology, the modeled total radiative intensity was compared to the total intensity measured with a narrow angle radiometer and the agreement in the results was good, supporting the validity of the used approach

Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification

A machine learning approach to modeling and predicting training effectiveness

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2015.Cataloged from PDF version of thesis.Includes bibliographical references (pages 357-372).Developments in online and computer-based training (CBT) technologies have enabled improvements in efficiency, efficacy, and scalability of modern training programs. The use of computer-based methods in training programs allows for the collection of trainee assessment metrics at much higher levels of detail, providing new opportunities for training evaluation in these programs. These resulting datasets may provide increased opportunities for training evaluation and trainee intervention through the use of descriptive and predictive modeling. In particular, there is the potential for descriptive approaches to provide greater understanding of trainee behavior and indicate similarities between trainees, while accurate prediction models of future performance available early in a training program could help inform trainee intervention methods. However, traditional analysis techniques and human intuition are of limited use on so-called "big-data" environments, and one of the most promising areas to prepare for this influx of complex training data is the field of machine learning. Thus, the objective of this thesis was to lay the foundations for the use of machine learning algorithms in computer-based training settings. First, a taxonomy of training domains was developed to identify typical properties of training data. Second, the theoretical and practical considerations between traditional machine learning applications and various training domains were identified and compared. This analysis identified the potential impacts of training data on machine learning performance and presented countermeasures to overcome some of the challenges associated with data from human training. Third, analyses of machine learning performance were conducted on datasets from two different training domains: a rule-based nuclear reactor CBT, and a knowledge-based classroom environment with online components. These analyses discussed the results of the machine learning algorithms with a particular focus on the usefulness of the model outputs for training evaluation. Additionally, the differences between machine learning applications to the two training domains were compared, providing a set of lessons for the future use of machine learning in training. Several consistent themes emerged from these analyses that can inform both research and applied use of machine learning in training. On the tested datasets, simple machine learning algorithms provided similar performance to complex methods for both unsupervised and supervised learning, and have additional benefits for ease of interpretation by training supervisors. The availability of process-level assessment metrics generally provided little improvement over traditional summative metrics when available, but were able to make strong contributions when summative information was limited. In particular, process-level information was able to improve early prediction to inform trainee intervention for longer training programs, and was able to improve descriptive modeling of the data for shorter programs. The frequency with which process-level information is collected further allows for accurate predictions to be made earlier in the training program, which allow for greater certainty and earlier application of targeted interventions in a training program. These lessons provide the groundwork for the study of machine learning on training domain data, enabling the efficient use of new data opportunities in computer-based training programs.by Alexander James Stimpson.Ph. D